{"id":"https://openalex.org/W7130568157","doi":"https://doi.org/10.48550/arxiv.2602.15923","title":"A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems","display_name":"A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems","publication_year":2026,"publication_date":"2026-02-17","ids":{"openalex":"https://openalex.org/W7130568157","doi":"https://doi.org/10.48550/arxiv.2602.15923"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15923","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15923","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.15923","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073099662","display_name":"Rakshit Kumar Singh","orcid":"https://orcid.org/0000-0002-1869-8772"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Rakshit Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118987801","display_name":"Aryan Amit Barsainyan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barsainyan, Aryan Amit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039641760","display_name":"Bharath Ramsundar","orcid":"https://orcid.org/0000-0001-8450-4262"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramsundar, Bharath","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12557","display_name":"Inorganic Chemistry and Materials","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/1604","display_name":"Inorganic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11804","display_name":"Quantum many-body systems","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.79830002784729},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.6924999952316284},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4618000090122223},{"id":"https://openalex.org/keywords/density-functional-theory","display_name":"Density functional theory","score":0.44940000772476196},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.44440001249313354},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.42500001192092896},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39579999446868896}],"concepts":[{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.79830002784729},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6924999952316284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6134999990463257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C152365726","wikidata":"https://www.wikidata.org/wiki/Q1048589","display_name":"Density functional theory","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.375},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36660000681877136},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3560999929904938},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3425999879837036},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2939000129699707},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.28299999237060547},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C29001434","wikidata":"https://www.wikidata.org/wiki/Q8084","display_name":"Trigonometry","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25119999051094055},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15923","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15923","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.15923","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15923","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6756488680839539,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Density":[0],"Functional":[1],"Theory":[2],"(DFT)":[3,50],"is":[4,92],"widely":[5],"used":[6],"for":[7,23,51,63,74,133],"first-principles":[8],"simulations":[9],"in":[10,30,72,94],"chemistry":[11],"and":[12,53,79,104,128,144],"materials":[13],"science,":[14],"but":[15],"its":[16],"computational":[17],"cost":[18],"remains":[19],"a":[20,38,60,97],"key":[21],"limitation":[22],"large":[24],"systems.":[25,56],"Motivated":[26],"by":[27],"recent":[28],"advances":[29],"ML-based":[31],"exchange-correlation":[32,76],"(XC)":[33,77],"functionals,":[34],"this":[35],"paper":[36],"introduces":[37],"differentiable":[39,103],"framework":[40,58,91],"that":[41,67],"integrates":[42],"machine":[43],"learning":[44,111],"models":[45,66,127,147],"into":[46],"density":[47],"functional":[48],"theory":[49],"solids":[52],"other":[54],"periodic":[55],"The":[57,90],"defines":[59],"clean":[61],"API":[62],"neural":[64],"network":[65],"can":[68],"act":[69],"as":[70],"drop":[71],"replacements":[73],"conventional":[75],"functionals":[78],"enables":[80],"gradients":[81],"to":[82,106,121,129],"flow":[83],"through":[84],"the":[85,115,118,123,131,152],"full":[86],"self-consistent":[87],"DFT":[88],"workflow.":[89],"implemented":[93],"Python":[95],"using":[96],"PyTorch":[98],"backend,":[99],"making":[100],"it":[101],"fully":[102],"easy":[105],"use":[107],"with":[108,117],"standard":[109],"deep":[110],"tools.":[112],"We":[113],"integrate":[114],"implementation":[116],"DeepChem":[119],"library":[120],"promote":[122],"reuse":[124],"of":[125,154],"established":[126,139],"lower":[130],"barrier":[132],"experimentation.":[134],"In":[135],"initial":[136],"benchmarks":[137],"against":[138],"electronic":[140],"structure":[141],"packages":[142],"(GPAW":[143],"PySCF),":[145],"our":[146],"achieve":[148],"relative":[149],"errors":[150],"on":[151],"order":[153],"5-10%.":[155]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-02-20T00:00:00"}
